Device control using audio data

ABSTRACT

An audio control system can control interactions with an application or device using keywords spoken by a user of the device. The audio control system can use machine learning models (e.g., a neural network model) trained to recognize one or more keywords. Which machine learning model is activated can depend on the active location in the application or device. Responsive to detecting keywords, different actions are performed by the device, such as navigation to a pre-specified area of the application.

PRIORITY CLAIM

This application is a continuation of U.S. patent application Ser. No.15/981,295, filed May 16, 2018, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to special-purpose machines andimprovements to such variants, and to the technologies by which suchspecial-purpose machines become improved compared to otherspecial-purpose machines for device control using terms detected inaudio data.

BACKGROUND

Some computers have limited computational resources. For example,smartphones generally have a relatively small screen size, limitedinput/output controls, and less memory and processor power than theirdesktop computer and laptop counterparts. Different issues arise wheninteracting with a computer with limited computational resources. Forexample, a user may have to drill-down into a number of menus instead ofusing a keyboard shortcut or simply viewing all the menus at once on alarger screen (e.g., a screen of a desktop). Further, navigating todifferent user interfaces, different menus, and selecting different userinterface elements can be computationally intensive, which can cause thedevice to lag and further unnecessarily drain the device's battery.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure (“FIG.”) number in which that element or act is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is a block diagram illustrating further details regarding themessaging system of FIG. 1, according to example embodiments.

FIG. 3 is a schematic diagram illustrating data that may be stored in adatabase of a messaging server system, according to certain exampleembodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeralmessage, and associated multimedia payload of data) or a contentcollection (e.g., an ephemeral message story) may be time-limited (e.g.,made ephemeral), according to some example embodiments.

FIG. 6 illustrates internal functional engines of an audio controlsystem, according to some example embodiments.

FIG. 7 shows a flow diagram of an example method for implementingapplication control using audio data, according to some exampleembodiments.

FIG. 8 shows an example functional architecture for implementingapplication control using model and user interface associations,according to some example embodiments.

FIG. 9 illustrates a finite state machine for initiating differentkeyword models, according to some example embodiments.

FIG. 10 shows an example configuration of a detection engineimplementing a neural network sub-engine, according to some exampleembodiments.

FIG. 11 shows an example embodiment of the detection engine implementinga template sub-engine, according to some example embodiments.

FIG. 12 shows a main window user interface on a display device of theclient device, according to some example embodiments.

FIG. 13 shows an example page user interface, according to some exampleembodiments.

FIG. 14 displays an example image capture user interface, according tosome example embodiments.

FIG. 15 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 16 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

As discussed, interacting with computer devices having limited resourcescan be problematic. To this end, an audio control system can beimplemented on the client device to perform actions in response todetecting keywords spoken by a user of the client device. The audiocontrol system can initiate one or more machine learning schemes trainedto detect sets of keywords. In some example embodiments, which machinelearning scheme is initiated depends on which user interface (UI) isbeing displayed on the client device. The machine learning schemes canbe implemented as neural networks that are trained to perform naturallanguage processing and keyword recognition.

Some embodiments of the machine learning schemes are implemented astemplate recognition schemes that recognize portions of audio data basedon those portions being similar to waveforms in a given template. Theaudio control system can implement machine learning schemes to navigateto a given area of an application, select an element in the application(e.g., a user interface element, a button), or cause actions to beperformed within the application or on the client device in response tokeywords being detected. In some example embodiments, the action orcontent displayed is pre-associated with the keyword of the differentmachine learning schemes.

In some example embodiments, each of the machine learning schemes isassociated with a set of one or more user interfaces, such that when oneof the user interfaces is displayed a corresponding machine learningscheme is activated in response. In some example embodiments, anindividual machine learning scheme or content associated with a machinelearning scheme is updated without updating the other machine learningschemes.

FIG. 1 shows a block diagram of an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network106. The messaging system 100 includes multiple client devices 102, eachof which hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via the network 106(e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed either by a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality within either the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108 and to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, social network information, and live event information, asexamples. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via user interfaces of themessaging client application 104.

Turning now specifically to the messaging server system 108, anapplication programming interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client devices 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., images or video) from a messaging client application 104 toa messaging server application 114 for possible access by anothermessaging client application 104; the setting of a collection of mediadata (e.g., a story); the retrieval of such collections; the retrievalof a list of friends of a user of a client device 102; the retrieval ofmessages and content; the adding and deletion of friends to and from asocial graph; the location of friends within the social graph; andopening application events (e.g., relating to the messaging clientapplication 104).

The application server 112 hosts a number of applications andsubsystems, including the messaging server application 114, an imageprocessing system 116, a social network system 122, and an update system123, in some example embodiments. The messaging server application 114implements a number of message-processing technologies and functions,particularly related to the aggregation and other processing of content(e.g., textual and multimedia content) included in messages receivedfrom multiple instances of the messaging client application 104. As willbe described in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available, by themessaging server application 114, to the messaging client application104. Other processor- and memory-intensive processing of data may alsobe performed server-side by the messaging server application 114, inview of the hardware requirements for such processing.

The application server 112 also includes the image processing system116, which is dedicated to performing various image processingoperations, typically with respect to images or video received withinthe payload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph (e.g., entity graph304 in FIG. 3) within the database 120. Examples of functions andservices supported by the social network system 122 include theidentification of other users of the messaging system 100 with whom aparticular user has relationships or whom the particular user is“following,” and also the identification of other entities and interestsof a particular user.

The update system 123 manages training and deployment of machinelearning schemes and models distributed to a plurality of client devices(e.g., client device 102). In some example embodiments, the updatesystem 123 trains the neural network models on sets of keywords to berecognized on the client device 102. The trained models are thendistributed as part of the messaging client application 104 downloaddiscussed below, or as an update to the messaging client application104.

The application server 112 is communicatively coupled to the databaseserver 118, which facilitates access to the database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of subsystems, namely an ephemeral timer system 202, a collectionmanagement system 204, an annotation system 206, an audio control system210, and a curation interface 208.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., a Story), selectively display and enableaccess to messages and associated content via the messaging clientapplication 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image, video, and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text, and audio) may be organized into an“event gallery” or an “event story.” Such a collection may be madeavailable for a specified time period, such as the duration of an eventto which the content relates. For example, content relating to a musicconcert may be made available as a “story” for the duration of thatmusic concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a geofilter or filter) tothe messaging client application 104 based on a geolocation of theclient device 102. In another example, the annotation system 206operatively supplies a media overlay to the messaging client application104 based on other information, such as social network information ofthe user of the client device 102. A media overlay may include audio andvisual content and visual effects. Examples of audio and visual contentinclude pictures, text, logos, animations, and sound effects. An exampleof a visual effect includes color overlaying. The audio and visualcontent or the visual effects can be applied to a media content item(e.g., a photo) at the client device 102. For example, the media overlayincludes text that can be overlaid on top of a photograph generated bythe client device 102. In another example, the media overlay includes anidentification of a location (e.g., Venice Beach), a name of a liveevent, or a name of a merchant (e.g., Beach Coffee House). The mediaoverlays may be stored in the database 120 and accessed through thedatabase server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map and upload content associated with the selectedgeolocation. The user may also specify circumstances under whichparticular content should be offered to other users. In another exampleembodiment, the annotation system 206 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the annotation system 206 associates the media overlay of ahighest-bidding merchant with a corresponding geolocation for apredefined amount of time.

FIG. 3 is a schematic diagram illustrating data 300, which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata 300 could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. An entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, and so forth. Regardless of type, any entity regardingwhich the messaging server system 108 stores data may be a recognizedentity. Each entity is provided with a unique identifier, as well as anentity type identifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between or among entities. Suchrelationships may be social, professional (e.g., work at a commoncorporation or organization), interest-based, or activity-based, forexample.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a Global Positioning System(GPS) unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include a current temperature at a specificlocation, a current speed at which a sending user is traveling, abattery life for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe message table 314. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a Story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhom a record is maintained in the entity table 302). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices 102 havelocation services enabled and are at a common location or event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104 based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102 and that is included        in the message 400.    -   A message image payload 406: image data captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message audio payload 410: audio data captured by a microphone        or retrieved from the memory component of the client device 102,        and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to the message image payload 406, message video        payload 408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, and message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, with each        of these parameter values being associated with respective        content items included in the content (e.g., a specific image in        the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: values identifying one or more        content collections (e.g., “stories”) with which a particular        content item in the message image payload 406 of the message 400        is associated. For example, multiple images within the message        image payload 406 may each be associated with multiple content        collections using identifier values.    -   A message tag 420: one or more tags, each of which is indicative        of the subject matter of content included in the message        payload. For example, where a particular image included in the        message image payload 406 depicts an animal (e.g., a lion), a        tag value may be included within the message tag 420 that is        indicative of the relevant animal. Tag values may be generated        manually, based on user input, or may be automatically generated        using, for example, image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of the message 400may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload 406may be a pointer to (or address of) a location within the image table308. Similarly, values within the message video payload 408 may point todata stored within the video table 310, values stored within the messageannotations 412 may point to data stored in the annotation table 312,values stored within the message story identifier 418 may point to datastored in the story table 306, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within the entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral), according to some example embodiments.

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone example, an ephemeral message 502 is viewable by a receiving userfor up to a maximum of ten seconds, depending on the amount of time thatthe sending user specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504. The ephemeral message story 504 has anassociated story duration parameter 508, a value of which determines atime duration for which the ephemeral message story 504 is presented andaccessible to users of the messaging system 100. The story durationparameter 508, for example, may be the duration of a music concert,where the ephemeral message story 504 is a collection of contentpertaining to that concert. Alternatively, a user (either the owninguser or a curator user) may specify the value for the story durationparameter 508 when performing the setup and creation of the ephemeralmessage story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message 502 may “expire” andbecome inaccessible within the context of the ephemeral message story504, prior to the ephemeral message story 504 itself expiring (via thestory timer 514) in terms of the story duration parameter 508.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In response to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (e.g., specifically, the messaging client application 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage story 504 to no longer be displayed within a user interface ofthe messaging client application 104.

FIG. 6 illustrates internal functional engines of an audio controlsystem 210, according to some example embodiments. As illustrated, audiocontrol system 210 comprises an interface engine 605, a detection engine610, an action engine 615, and a content engine 620. The interfaceengine 605 manages displaying user interfaces and initiating a keyworddetection model on the detection engine 610. In some exampleembodiments, the interface engine 605 is configured to initiaterecording of audio data using a transducer (e.g., a microphone) of theclient device 102. Further, in some example embodiments, the interfaceengine 605 is configured to capture one or more images using an imagesensor of the client device 102. The images can include an image, video,or live video that is dynamically updated or displayed on a displaydevice of the client device 102.

The detection engine 610 manages detecting keywords in audio data usingone or more machine learning schemes. In some example embodiments, thedetection engine 610 implements neural networks trained on differentsets of keywords to detect a keyword spoken by user and captured inaudio data. In some example embodiments, the detection engine 610 uses atemplate engine that matches portions of the audio data to templates todetect keywords.

The action engine 615 is configured to perform one or more actions inresponse to a keyword detected by the detection engine 610. For example,in response to a keyword being detected in the audio data, the actionengine 615 can trigger the interface engine 605 to capture an imageusing the image sensor of the client device 102.

As another example, responsive to the detection engine 610 detecting akeyword in the audio data, the action engine 615 can apply an imageeffect or video filter effect to one or more images currently beingdisplayed on the display device of the client device 102. Further, as anadditional example, in response to a keyword being detected in the audiodata, the action engine 615 can navigate to a user interface of themessaging client application 104, select a user interface element withinthe messaging client application 104, or navigate to external networksites (e.g. websites on the Internet).

The content engine 620 manages displaying content pre-associated withthe detected keyword. For example, in response to a keyword beingdetected in audio data, the content engine 620 may display userinterface content as an overlay on an image or video being displayed onthe display device of the client device 102. The image or video with theoverlay content can be shared as an ephemeral message 502 on a networksite, such as a social media network site as discussed above.

FIG. 7 shows a flow diagram of an example method 700 for efficientlycontrolling a computationally limited device (e.g., a smartphone) usingan audio control system, according to some example embodiments. Atoperation 705, the interface engine 605 displays a user interface. Forexample, at operation 705, the interface engine 605 navigates from amain window of an application active on the client device 102 to animage capture user interface of the application active on the clientdevice 102.

At operation 710, the detection engine 610 activates a keyword detector,such as a machine learning scheme, that is configured to detectkeywords. For example, at operation 710, in response to the imagecapture user interface being displayed at operation 705, the detectionengine 610 initiates a recurrent neural network (RNN) trained to detectuser-spoken keywords in audio data. In some example embodiments, thekeyword detector initiated by the detection engine 610 is a templateengine that can match waveforms in recorded audio data to keywordtemplate waveforms.

At operation 715, the interface engine 605 records audio data using amicrophone of the client device 102. In some example embodiments, atoperation 715, the interface engine 605 records the audio data in atemporary memory that may capture a subset of audio data to conservememory of the client device 102. For example, thirty-seconds of audiodata can be recorded to the buffer, and as new audio data is captured,audio data that is older than thirty seconds is delated or otherwiseremoved from the buffer. Thirty seconds is used as an exampletime-period in which the buffer can capture audio data; it isappreciated that in some example embodiments, other time periods (e.g.,five seconds, ten seconds, fourth-five seconds) can likewise beimplemented. Further, although operation 715 in which audio data isrecorded is placed after operation 710 in which the keyword detector isactivated, it is appreciated that in some example embodiments, operation710 and 715 reverse order with the audio data being recorded to a bufferfirst and then the keyword detector is activated. Likewise, in someexample embodiments, the operations of 710 and 715 can be initiatedapproximately at the same time in response to a user interface beingdisplayed.

At operation 720, the detection engine 610 detects one or more keywordsin the audio data. For example, at operation 720, the detection engine610 implements a neural network to detect that the user of the clientdevice has spoken the keyword “Havarti” (a type of cheese).

At operation 725, in response to the keyword being detected at operation720, pre-associated content is displayed on the display device of theclient device 102. For example, in response to the keyword “Havarti”being detected, the action engine 615 displays a cartoon picture of ablock of Havarti cheese on the display device of the client device 102.Which content is pre-associated with which keyword can be tracked in adata structure stored in memory of the client device 102, as discussedin further detail below with reference to FIG. 8.

In some example embodiments, at operation 725, the action engine 615performs one or more actions to display the content at operation 725.For example, at operation 725, in response to the keyword “Havarti”being detected, the action engine 625 captures an image using a cameraon the client device 102, and further displays the captured image as thecontent of operation 725. As a further example, at operation 725, inresponse to the keyword “Havarti” being detected, the action engine 625navigates from a current user interface to another user interface (e.g.,a user interface configured to enable the user to take pictures usingthe client device 102).

In some example embodiments, operation 725 is omitted and content is notdisplayed. In those example embodiments, instead of displaying content,one or more background actions can be performed in response to a keywordbeing detected. For instance, in response to detecting the keyword“save” spoken by the user holding the client device 102, the actionengine 615 may save the state of a document, image, or other object in amemory of the client device 102.

FIG. 8 shows an example functional architecture 800 for implementingapplication control using model and user interface associations,according to some example embodiments. In architecture 800, a pluralityof user interfaces 805 of an application are displayed. The applicationcan be an application that controls other applications (e.g., operatingsystem 1502, discussed below), or an application operating on top of anoperating system, such as messaging client application 104. Theplurality of user interfaces 805 include a main window 810, a post 815,a page 820, and an image capture user interface 825. The main window 810is an example primary UI for the messaging client application 104 (e.g.,a “home” screen). The post 815 is a UI of an example UI of an ephemeralmessage (e.g., ephemeral message 502), as discussed above. The page 820can be a UI of a network page, web article, or other network item thatis generally viewable for a longer period of time in the messagingclient application 104 than the period of time in which the post 815 canbe viewed. The image capture user interface 825 is a UI configured tocapture images using an image sensor of the client device 102. Althoughonly four user interfaces are discussed in FIG. 8, it is appreciatedthat in some example embodiments other user interfaces can be includedin a similar manner.

In some example embodiments, different sets of keywords are associatedwith different user interfaces being displayed by the messaging clientapplication 104. For example, architecture 800 displays models 835, 845,and 855. Each of the models are machine learning schemes trained ondifferent sets of keywords. In the example illustrated, global model 835is a machine learning scheme trained to detect a single keyword,“cheese”. In response to detecting the single keyword, the action engine615 or the content engine 620 implement item 840A, which ispre-associated with the keyword “cheese”. For example, upon the keyword“cheese” being detected, the action engine 615 can cause the interfaceengine 605 to capture an image using an image sensor of the clientdevice 102.

The multiscreen model 845 is a machine learning scheme trained to detectthe keywords: “cheese”, “surprise”, “woof”, and “lens 5”. As with theglobal model 835, in response to the machine learning scheme of themultiscreen model 845 detecting any of its keywords, items 840A-840D canbe implemented by the action engine 615 or the content engine 620. Forexample, the keyword “surprise” can be configured as a daily surprise inthe messaging client application 104 (e.g., new image effects, new userinterface content, new application functionality, coupons, and so on). Auser of the client device 102 can open the messaging client application104 and speak the word “surprise” to cause pre-associated item 840B tobe implemented. Similarly, in response to the keyword “woof” beingdetected, item 840C can be implemented (e.g., in response to “woof”being detected, apply cartoon dog ears as an overlay to an image of theuser displayed on the client device 102). Similarly, in response to thekeyword “lens 5” being detected, item 840D can be implemented (e.g., inresponse to “lens 5” being detected, select the fifth UI element thatmay be offscreen, thereby saving the user from navigation through one ormore menus).

The page model 855 is a machine learning scheme trained to detect thekeywords: “cheese”, “surprise”, “woof”, “lens 5”, and “acme”. As withthe global model 835 and the multiscreen model 845, in response to themachine learning scheme of the page model 855 detecting any of itskeywords, items 840A-840E can be implemented by the action engine 615 orthe content engine 620. For example, in response to detecting thekeyword “acme” while a given user interface is active, item 840E can beimplemented (e.g., while a given user interface is being displayed, inresponse to “acme” being detected, display user interface content thatis associated with a company called Acme). In some example embodiments,the page model and associated content and actions (e.g., item 840E) areupdated without updating the other models or items. In this way, itemsof the narrower page model can be updated without updating the othermodels so that content activated on a given user interface can beefficiently managed. In some example embodiments, the different modelsuse the same content. For example, each of the models may include apointer to a location in memory in which content or actions associatedwith the term “surprise” is stored (i.e., a memory location of item840B). Thereby enabling efficient updates of content or actionsassociated with several models.

Each of the models may be associated with one or more of the pluralityof user interfaces 805. For example, global model 835 is associated withgroup 833, including post 815, page 820, and image capture userinterface 825. When a user navigates to any one of those userinterfaces, the global model 835 is activated by initiating a neuralnetwork or template trained to detect keywords pre-selected for theglobal model 835. Likewise, multiscreen model 845 is associated withgroup 830, including page 820 and image capture user interface 825.Likewise, page model 855 is associated only with page 820, according tosome example embodiments. In response to a given user interface of theplurality of user interfaces 805 being displayed on the client device102, an associated model is activated. In this way, different sets ofuser interfaces of the messaging client application 104 can bepre-associated with different models.

In some example embodiments, the global model 835 is a model associatedwith the greatest amount or all of the user interfaces of the messagingclient application 104. For example, if a global model is associatedwith all the user interfaces of the messaging client application 104then when the messaging client application 104 is initiated, the globalmodel 835 is activated, thereby enabling detection of keywords spoken bya user and control actions anywhere in the messaging client application104.

In some example embodiments, the multiscreen model 845 is a modelassociated with multiple user interfaces of the messaging clientapplication 104. In this way, if a set of user interfaces of anapplication are of a similar type (e.g., have similar or the samefunctionality), the multiscreen model 845 is activated to enable theuser to perform similar actions in any of the user interfaces that areof the similar type.

The page model 855 is a model associated with a single user interface.In this way, if content or application actions should only be madeavailable within a specific single page, the model 855 is activated whenthe single page is displayed.

Further, in some example embodiments, the sets of keywords for which themodels are trained overlap so that when a narrower model is activated,the functionality of the broader higher-level model is maintained. Forexample, multiscreen model 845 is trained to detect the same to keywordsincluded in the global model 835 plus three additional keywords. Thus,if a user navigates from post 815, which is associated with the globalmodel 835, to page 820, which is associated with the multiscreen model845, the user can still control the application using the keyword forwhich the two models are both trained (e.g., “cheese”), thereby enablinga seamless user experience.

In some example embodiments, each of the models are trained on differentsets of keywords. For example, page model 855 can be trained for onekeyword, and another page model (not depicted) can be trained to detectanother keyword. Further, in some example embodiments, multiplemultiscreen models that are trained on entirely different sets ofkeywords can likewise be implemented.

In some example embodiments, as discussed above, when any of the models835, 845, and 855 are activated, the interface engine 605 may initiate amicrophone and input audio data into the activated model for keyworddetection.

FIG. 9 illustrates a finite state machine 900 for initiating differentkeyword models, according to some example embodiments. The finite statemachine 900 can be implemented within the interface engine 605, whichcauses the detection engine 610 to activate and deactivate differentmodels in response to a user navigating to different user interfaces. Insome example embodiments, the finite state machine 900 is integrated inan operating system, and navigation of UIs can be controlled in asimilar manner. In the example illustrated, the finite state machine 900includes four states: an inactive state 905, a global model state 910, apage model state 915, and a multiscreen model state 920. In the inactivestate 905, no audio data is recorded and all of the models are inactive.As indicated by loop 925, as long as none of the user interfacesassociated with the other states are displayed, the messaging clientapplication 104 remains in the inactive state 905.

If the user navigates to one or more of the user interfaces associatedwith the global model, the application transitions 945 to the globalmodel state 910. In the global model state 910, the machine learningscheme trained on the global model is activated. Further, in the globalmodel state 910, the interface engine 605 records a portion of audiodata in temporary memory (e.g., a thirty second audio data buffer) forinput into the global model. As indicated by loop 930, as long as userinterfaces associated with the global model state 910 are displayed orotherwise active in the messaging client application 104, theapplication 114 remains in the global model state 910.

If the user navigates to one or more of the user interfaces associatedwith the multiscreen model, the application transitions 950 to themultiscreen model state 920. In the multiscreen model state 920, theother models (e.g. global model, page model) are deactivated and themultiscreen model is made active (e.g., a neural network trained todetect keywords of the multiscreen model is activated). Further, in themultiscreen model state 920, the interface engine 605 records last 30seconds of audio data for input into the multiscreen model. As indicatedby loop 940, as long as user interfaces associated with the multiscreenmodel state 920 are displayed or otherwise active in messaging clientapplication 104, the messaging client application 104 remains in themultiscreen model state 920.

If the user navigates to one or more user interfaces associated with thepage model state 915, the application transitions 955 to the page modelstate 915. In the page model state 915, the other models (e.g. theglobal model, the multiscreen model) are deactivated, and the page modelis active (e.g. a neural network trained to detect keywords in the pagemodel is activated). Further, in the page model state 915, the interfaceengine 605 records the last 30 seconds of audio data for input into thepage model state 915. As indicated by loop 935, as long as userinterfaces associated with the page model state 915 are displayed orotherwise active in messaging client application 104, the messagingclient application 104 remains in the page model state 915.

If the user navigates to one or more user interfaces that are notassociated with any of the models, the messaging client application 104transitions 960 to the inactive state 905 and recording of audio data isterminated. Further, as indicated by transitions 970 and 965, themessaging client application 104 may transition between the differentstates and/or skip models. For example, the messaging client application104 can transition from the global model state 910 to the page modelstate 915 through transition 965.

FIG. 10 shows an example configuration of the detection engine 610implementing a neural network sub-engine 1005, according to some exampleembodiments. In some example embodiments, the audio data 1010 generatedby the microphone is converted from waveform into a visualrepresentation, such as a spectrogram. In some example embodiments, theaudio data 1010 is input into a neural network 1015 for classification.

As illustrated in FIG. 10, in some example embodiments, the neuralnetwork engine 1015 implements a recurrent neural network (RNN) todetect keywords. A recurrent neural network is a neural network thatshares weight data of the connections across several time steps. Thatis, each member of an output node is a function of the previous membersof that output node. Each member of the output is produced using thesame update rule applied to the previous outputs. In some exampleembodiments, the recurrent neural network is implemented as abidirectional recurrent neural network that includes a first RNN thatmoves forward in time (e.g., considering words from the beginning of thesentence to the end of the sentence) and another RNN that movesbackwards in time (e.g., processing words from the end of a sentence tothe beginning of a sentence). Further, in some example embodiments, theRNN of neural network 1015 implements long short-term memory (LS™),which are self loops that produce paths where the gradient can flow fromlonger durations, as is appreciated by those having ordinary skill inthe art.

In some example embodiments, the neural network 1015 is configured as aconvolutional neural network (CNN) to detect keywords by analyzingvisual representations of the keywords (e.g., a spectrogram). A CNN is aneural network configured to apply different kernel filters to an imageto generate a plurality of feature maps which can then be processed toidentify and classify characteristics of an image (e.g., object featuredetection, image segmentation, and so on). As illustrated in FIG. 10, insome example embodiments, a portion of the audio data generated by themicrophone of the client device is converted into a visualrepresentation, such as a spectrogram, which is then portioned intoslices and input into the neural network 1015 for processing.

As illustrated in FIG. 10, the audio data 1010 is input into the neuralnetwork 1015, which outputs the detected keyword 1020. Although onekeyword (“lens 5”) is displayed in FIG. 10, it is appreciated by thosehaving ordinary skill in the art that the neural network 1015 can outputa classification score for each of the keywords for which the neuralnetwork is trained. In some example embodiments, the keyword that hashighest classification score is output as a detected keyword (e.g.,detected keyword 1020).

FIG. 11 shows an example embodiment of the detection engine 610implementing a template sub-engine 1100, according to some exampleembodiments. The template sub-engine 1100 uses a plurality of waveformtemplates to detect keywords, such as waveform template 1105, whichdetects two keywords: “lens 5” and “cheese”. In the example embodimentillustrated in FIG. 11, the keyword “lens 5” activates a video filter,and the keyword “cheese” captures an image of the user with the videofilter activated. The template sub-engine 1100 can receive a portion ofaudio data 1110 and determine that the shape of waveforms in audio data1110 is similar to the shape of wave forms in a waveform template 1105,thereby detecting one or more keywords.

FIG. 12 shows a main window user interface 1210 on a display device 1205of the client device 102, according to some example embodiments. Themain window user interface 1210 includes a page area 1215 that displaysthumbnails that are links to a plurality of pages. If a user 1200selects one of the thumbnails (e.g., the thumbnail “Page 1”), then themessaging client application 104 displays the page linked to thethumbnail. The main window user interface 1210 further includes a postarea 1220 that displays a plurality of post links that link to differentephemeral messages published by a network site. If the user 1200 selectsone of the post links, the messaging client application 104 displays theassociated ephemeral message on the display device 1205. Further, themain window user interface 1210 includes a camera user interface element1225 (e.g., a selectable button). If the user 1200 selects the camerauser interface element 1225, the application displays a camera captureuser interface, in which the user can generate one or more images usingan image sensor 1227. In some example embodiments, while the main windowuser interface 1210 is displayed, the application 1014 is in an inactivestate in which no audio is recorded and no keyword model is activated.Further, in some example embodiments, the global model is activated sothat the user can initiate an image capture using the image sensor 1227anywhere in the messaging client application 104.

FIG. 13 shows an example page user interface 1300, according to someexample embodiments. The page user interface 1300 is displayed inresponse to the user 1200 selecting one of the page thumbnails in thepage area 1215. In some example embodiments, when the user 1200navigates to any of the pages, the multiscreen model is activated todetect keywords spoken by the user 1200. For example, in response to theuser 1200 speaking a keyword detected by the multiscreen model, the userinterface content 1305 may be displayed in the user interface 1300. As afurther example, while the page user interface 1300 is displayed, if theuser 1200 speaks one of the keywords of the multiscreen model, theaction engine can cause the client device 102 to navigate to an externalwebsite (e.g., an external website of a company/organization thatcreated or published the “Cheshire Social” page).

FIG. 14 displays an example image capture user interface 1400, accordingto some example embodiments. The image capture user interface 1400 canbe displayed in response to the user 1200 selecting the camera userinterface element 1225 or verbally speaking a keyword detected by theglobal model (e.g., “cheese”). The image capture user interface 1400further displays a plurality of filter buttons 1405 and a carousel,which the user 1200 can scroll through using a swipe gesture. Theplurality of filter buttons 1405 include: “B1”, “B2”, “B3”, “B4”, andadditional filter buttons such as “B5”, “B25” that are offscreen, andnot viewable on the display device 1205. In some example embodiments, toquickly navigate to objects that are offscreen and not viewable on thedisplay device 1205, the user 1200 can verbally speak “lens 5”, whichcan be detected by the page model active for the image capture userinterface 1400. In response to the “lens 5” keyword being detected, theaction engine 615 scrolls the carousel so that “B5” is viewable on thedisplay device 1205. Further, the action engine 615 may automaticallycause a video filter correlated with the filter button “B5” to beapplied to the image being displayed in the image capture user interface1400.

Further displayed in image capture user interface 1400 is a userinterface hint 1410, which can prompt the user 1200 to verbally speakone or more terms to cause additional actions on the messaging clientapplication 104. For example, the user interface hint 1410 can includethe sentence “What sound does a cat make?” If the user 1200 verballyspeaks “meow”, additional UI content or actions may be performed by theaudio control system 210, as discussed above.

FIG. 15 is a block diagram illustrating an example software architecture1506, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 15 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1506 may execute on hardwaresuch as a machine 1600 of FIG. 16 that includes, among other things,processors, memory, and input/output (I/O) components. A representativehardware layer 1552 is illustrated and can represent, for example, themachine 1600 of FIG. 16. The representative hardware layer 1552 includesa processing unit 1554 having associated executable instructions 1504.The executable instructions 1504 represent the executable instructionsof the software architecture 1506, including implementation of themethods, components, and so forth described herein. The hardware layer1552 also includes a memory/storage 1556, which also has the executableinstructions 1504. The hardware layer 1552 may also comprise otherhardware 1558.

In the example architecture of FIG. 15, the software architecture 1506may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1506may include layers such as an operating system 1502, libraries 1520,frameworks/middleware 1518, applications 1516, and a presentation layer1514. Operationally, the applications 1516 and/or other componentswithin the layers may invoke API calls 1508 through the software stackand receive a response in the form of messages 1512. The layersillustrated are representative in nature, and not all softwarearchitectures have all layers. For example, some mobile orspecial-purpose operating systems may not provide aframeworks/middleware 1518, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1502 may manage hardware resources and providecommon services. The operating system 1502 may include, for example, akernel 1522, services 1524, and drivers 1526. The kernel 1522 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1522 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1524 may provideother common services for the other software layers. The drivers 1526are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1526 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1520 provide a common infrastructure that is used by theapplications 1516 and/or other components and/or layers. The libraries1520 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 1502 functionality (e.g., kernel 1522,services 1524, and/or drivers 1526). The libraries 1520 may includesystem libraries 1544 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1520 may include API libraries 1546 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, or PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 1520 may also include a wide variety ofother libraries 1548 to provide many other APIs to the applications 1516and other software components/modules.

The frameworks/middleware 1518 provide a higher-level commoninfrastructure that may be used by the applications 1516 and/or othersoftware components/modules. For example, the frameworks/middleware 1518may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1518 may provide a broad spectrum of other APIsthat may be utilized by the applications 1516 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 1502 or platform.

The applications 1516 include built-in applications 1538 and/orthird-party applications 1540. Examples of representative built-inapplications 1538 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1540 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1540 may invoke the API calls 1508 provided bythe mobile operating system (such as the operating system 1502) tofacilitate functionality described herein.

The applications 1516 may use built-in operating system functions (e.g.,kernel 1522, services 1524, and/or drivers 1526), libraries 1520, andframeworks/middleware 1518 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 1514. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 16 is a block diagram illustrating components of a machine 1600,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 16 shows a diagrammatic representation of the machine1600 in the example form of a computer system, within which instructions1616 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1600 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1616 may be used to implement modules or componentsdescribed herein. The instructions 1616 transform the general,non-programmed machine 1600 into a particular machine 1600 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1600 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1600 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1600 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1616, sequentially or otherwise, that specify actions to betaken by the machine 1600. Further, while only a single machine 1600 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1616 to perform any one or more of the methodologiesdiscussed herein.

The machine 1600 may include processors 1610, memory/storage 1630, andI/O components 1650, which may be configured to communicate with eachother such as via a bus 1602. The memory/storage 1630 may include amemory 1632, such as a main memory, or other memory storage, and astorage unit 1636, both accessible to the processors 1610 such as viathe bus 1602. The storage unit 1636 and memory 1632 store theinstructions 1616 embodying any one or more of the methodologies orfunctions described herein. The instructions 1616 may also reside,completely or partially, within the memory 1632, within the storage unit1636, within at least one of the processors 1610 (e.g., within theprocessor cache memory accessible to processors 1612 or 1614), or anysuitable combination thereof, during execution thereof by the machine1600. Accordingly, the memory 1632, the storage unit 1636, and thememory of the processors 1610 are examples of machine-readable media.

The I/O components 1650 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1650 that are included in a particular machine 1600 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1650 may include many other components that are not shown inFIG. 16. The I/O components 1650 are grouped according to functionalitymerely for simplifying the following discussion, and the grouping is inno way limiting. In various example embodiments, the I/O components 1650may include output components 1652 and input components 1654. The outputcomponents 1652 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1654 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1650 may includebiometric components 1656, motion components 1658, environmentcomponents 1660, or position components 1662, among a wide array ofother components. For example, the biometric components 1656 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1658 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1660 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gassensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1662 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1650 may include communication components 1664operable to couple the machine 1600 to a network 1680 or devices 1670via a coupling 1682 and a coupling 1672, respectively. For example, thecommunication components 1664 may include a network interface componentor other suitable device to interface with the network 1680. In furtherexamples, the communication components 1664 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1670 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1664 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1664 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional barcodes such as Universal Product Code (UPC) barcode,multi-dimensional barcodes such as Quick Response (QR) code, Aztec code,Data Matrix, Dataglyph, MaxiCode, PDF418, Ultra Code, UCC RSS-2Dbarcode, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1664, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions 1616 forexecution by the machine 1600, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such instructions 1616. Instructions 1616 may betransmitted or received over the network 1680 using a transmissionmedium via a network interface device and using any one of a number ofwell-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine 1600 thatinterfaces to a network 1680 to obtain resources from one or more serversystems or other client devices 102. A client device 102 may be, but isnot limited to, a mobile phone, desktop computer, laptop, PDA,smartphone, tablet, ultrabook, netbook, multi-processor system,microprocessor-based or programmable consumer electronics system, gameconsole, set-top box, or any other communication device that a user mayuse to access a network 1680.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network 1680 that may be an ad hoc network, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network 1680 may include a wireless or cellular network,and the coupling 1682 may be a Code Division Multiple Access (CDMA)connection, a Global System for Mobile communications (GSM) connection,or another type of cellular or wireless coupling. In this example, thecoupling may implement any of a variety of types of data transfertechnology, such as Single Carrier Radio Transmission Technology(1×RTT), Evolution-Data Optimized (EVDO) technology, General PacketRadio Service (GPRS) technology, Enhanced Data rates for GSM Evolution(EDGE) technology, third Generation Partnership Project (3GPP) including3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High-Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long-TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long-range protocols, or other data transfertechnology.

“EMPHEMERAL MESSAGE” in this context refers to a message 400 that isaccessible for a time-limited duration. An ephemeral message 502 may bea text, an image, a video, and the like. The access time for theephemeral message 502 may be set by the message sender. Alternatively,the access time may be a default setting or a setting specified by therecipient. Regardless of the setting technique, the message 400 istransitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions 1616 and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions 1616. The term “machine-readable medium”shall also be taken to include any medium, or combination of multiplemedia, that is capable of storing instructions 1616 (e.g., code) forexecution by a machine 1600, such that the instructions 1616, whenexecuted by one or more processors 1610 of the machine 1600, cause themachine 1600 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor 1612 ora group of processors 1610) may be configured by software (e.g., anapplication or application portion) as a hardware component thatoperates to perform certain operations as described herein. A hardwarecomponent may also be implemented mechanically, electronically, or anysuitable combination thereof. For example, a hardware component mayinclude dedicated circuitry or logic that is permanently configured toperform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application-specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor or other programmable processor. Onceconfigured by such software, hardware components become specificmachines (or specific components of a machine 1600) uniquely tailored toperform the configured functions and are no longer general-purposeprocessors 1610. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.

Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processor 1612configured by software to become a special-purpose processor, thegeneral-purpose processor 1612 may be configured as respectivelydifferent special-purpose processors (e.g., comprising differenthardware components) at different times. Software accordingly configuresa particular processor 1612 or processors 1610, for example, toconstitute a particular hardware component at one instance of time andto constitute a different hardware component at a different instance oftime.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between or among suchhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1610 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1610 may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors1610. Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor 1612 or processors1610 being an example of hardware. For example, at least some of theoperations of a method may be performed by one or more processors 1610or processor-implemented components. Moreover, the one or moreprocessors 1610 may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines 1600including processors 1610), with these operations being accessible via anetwork 1680 (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors 1610, not only residing within asingle machine 1600, but deployed across a number of machines 1600. Insome example embodiments, the processors 1610 or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexample embodiments, the processors 1610 or processor-implementedcomponents may be distributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor1612) that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine 1600.A processor may, for example, be a central processing unit (CPU), areduced instruction set computing (RISC) processor, a complexinstruction set computing (CISC) processor, a graphics processing unit(GPU), a digital signal processor (DSP), an ASIC, a radio-frequencyintegrated circuit (RFIC), or any combination thereof. A processor 1610may further be a multi-core processor 1610 having two or moreindependent processors 1612, 1614 (sometimes referred to as “cores”)that may execute instructions 1616 contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

What is claimed is:
 1. A method comprising: displaying, on a display ofa device, a first user interface and a second user interface of anapplication that are active on the device; in response to the first userinterface and the second user interface being displayed, storing, in amemory of the device, audio data generated from a transducer of thedevice; identifying a first machine learning scheme corresponding to thefirst user interface being displayed, and a second machine learningscheme corresponding the second user interface being displayed, thefirst machine learning scheme comprising a first machine learning modelthat is trained to detect a first set of keywords in the audio data, thesecond machine learning scheme comprising a second machine learningmodel that is trained to detect a second set of keywords in the audiodata; detecting, using the first machine learning scheme and the secondmachine learning scheme, a portion of the audio data as one of the firstset of keywords or one of the second set of keywords; and in response todetecting the portion of the audio data as one of the first set ofkeywords or one of the second set of keywords, displaying user interfacecontent pre-associated with one of the first set of keywords or one ofthe second set of keywords.
 2. The method of claim 1, wherein the firstset keywords is different and larger than the second set of keywords. 3.The method of claim 1, further comprising: detecting that the first userinterface and the second user interface are displayed on the device; andin response to detecting that the first user interface and the seconduser interface are displayed on the device, activating the first machinelearning scheme and the second machine learning scheme.
 4. The method ofclaim 1, further comprising: detecting that the first user interface isdisplayed on the device and the second user interface is not displayedon the device; and in response to detecting that the first userinterface is displayed on the device and the second user interface isnot displayed on the device, activating the first machine learningscheme and deactivating the second machine learning scheme.
 5. Themethod of claim 1, wherein the first user interface or the second userinterface comprises one of a post user interface of an ephemeralmessage, a page user interface of a non-ephemeral message, or an imagecapture user interface.
 6. The method of claim 5, wherein the firstmachine learning scheme or the second machine learning scheme comprisesone of a global model, a multi-screen model, or a page model.
 7. Themethod of claim 6, wherein the global model is mapped to the post userinterface, wherein the multi-screen model is mapped to the page userinterface the image capture user interface, wherein the page model ismapped to the page user interface.
 8. The method of claim 1, furthercomprising: displaying, on the display of the device, a third userinterface; in response to displaying the third user interface,activating a third machine learning scheme on the device, the thirdmachine learning scheme being trained to detect an additional keywordthat is not in included in the first set of keywords and the second setof keywords; and detecting, using the third machine learning scheme, anadditional portion of the audio data as the additional keyword; and inresponse to detecting the additional portion of the audio data as theadditional keyword, displaying additional user interface contentpre-associated with the additional keyword.
 9. The method of claim 1,wherein the application comprises a messaging application, wherein thefirst machine learning scheme is a recurrent neural network configuredto process audio data.
 10. The method of claim 1, wherein displayinguser interface content includes displaying an image effect on one ormore images that are captured using a camera of the device.
 11. A systemcomprising: one or more processors of a device; and a memory storinginstructions that, when executed by the one or more processors, causethe system to perform operations comprising: displaying, on a display ofthe device, a first user interface and a second user interface of anapplication that are active on the device; in response to the first userinterface and the second user interface being displayed, storing, in amemory of the device, audio data generated from a transducer of thedevice; identifying a first machine learning scheme corresponding to thefirst user interface being displayed, and a second machine learningscheme corresponding the second user interface being displayed, thefirst machine learning scheme comprising a first machine learning modelthat is trained to detect a first set of keywords in the audio data, thesecond machine learning scheme comprising a second machine learningmodel that is trained to detect a second set of keywords in the audiodata; detecting, using the first machine learning scheme and the secondmachine learning scheme, a portion of the audio data as one of the firstset of keywords or one of the second set of keywords; and in response todetecting the portion of the audio data as one of the first set ofkeywords or one of the second set of keywords, displaying user interfacecontent pre-associated with one of the first set of keywords or one ofthe second set of keywords.
 12. The system of claim 11, wherein thefirst set keywords is different and larger than the second set ofkeywords.
 13. The system of claim 11, wherein the operations furthercomprise: detecting that the first user interface and the second userinterface are displayed on the device; and in response to detecting thatthe first user interface and the second user interface are displayed onthe device, activating the first machine learning scheme and the secondmachine learning scheme.
 14. The system of claim 11, wherein theoperations further comprise: detecting that the first user interface isdisplayed on the device and the second user interface is not displayedon the device; and in response to detecting that the first userinterface is displayed on the device and the second user interface isnot displayed on the device, activating the first machine learningscheme and deactivating the second machine learning scheme.
 15. Thesystem of claim 11, wherein the first user interface or the second userinterface comprises one of a post user interface of an ephemeralmessage, a page user interface of a non-ephemeral message, or an imagecapture user interface.
 16. The system of claim 15, wherein the firstmachine learning scheme or the second machine learning scheme comprisesone of a global model, a multi-screen model, or a page model.
 17. Thesystem of claim 16, wherein the global model is mapped to the post userinterface, wherein the multi-screen model is mapped to the page userinterface the image capture user interface, wherein the page model ismapped to the page user interface.
 18. The system of claim 11, whereinthe operations further comprise: displaying, on the display of thedevice, a third user interface; in response to displaying the third userinterface, activating a third machine learning scheme on the device, thethird machine learning scheme being trained to detect an additionalkeyword that is not in included in the first set of keywords and thesecond set of keywords; and detecting, using the third machine learningscheme, an additional portion of the audio data as the additionalkeyword; and in response to detecting the additional portion of theaudio data as the additional keyword, displaying additional userinterface content pre-associated with the additional keyword.
 19. Thesystem of claim 11, wherein the application comprises a messagingapplication, wherein the first machine learning scheme is a recurrentneural network configured to process audio data, and wherein displayinguser interface content includes displaying an image effect on one ormore images that are captured using a camera of the device.
 20. Anon-transitory machine-readable storage device embodying instructionsthat, when executed by a device, cause the device to perform operationscomprising: displaying, on a display of the device, a first userinterface and a second user interface of an application that are activeon the device; in response to the first user interface and the seconduser interface being displayed, storing, in a memory of the device,audio data generated from a transducer of the device; identifying afirst machine learning scheme corresponding to the first user interfacebeing displayed, and a second machine learning scheme corresponding thesecond user interface being displayed, the first machine learning schemecomprising a first machine learning model that is trained to detect afirst set of keywords in the audio data, the second machine learningscheme comprising a second machine learning model that is trained todetect a second set of keywords in the audio data; detecting, using thefirst machine learning scheme and the second machine learning scheme, aportion of the audio data as one of the first set of keywords or one ofthe second set of keywords; and in response to detecting the portion ofthe audio data as one of the first set of keywords or one of the secondset of keywords, displaying user interface content pre-associated withone of the first set of keywords or one of the second set of keywords.